roadmap.md

Metta AI Research Roadmap

1. Environment Development

1.1. Game Dynamics

• 1.1.1. Resource System

  • Resource generation rates
  • Rarity
  • Mining cost
  • Probabilistic mining

• 1.1.2. Metabolism and Energy Costs

  • Converting between resources
  • Converting to energy

• 1.1.3. Object Interactions and Crafting

• 1.1.4. Open-ended Game Rules

1.2. Agent Diversity

• 1.2.1. Species with different energy costs
• 1.2.2. Upgradable Traits and Skills
  • Reduced energy costs
  • Increased damage / shield / attack range
  • Noise reduction of observations
  • Energy regen
  • Explicit upgrade vs experience
  • Balancing upgrade costs
• 1.2.3. Equipment and Inventory Management
  • Items provide upgrades and are tradeable

1.3. Terrain Generation

• 1.3.1. Procedural World Generation
• 1.3.2. Environmental Hazards and Obstacles
  • Energy costs for travel
  • Attack / defense bonus
• 1.3.3. Seasonal Changes and Dynamic Weather
  • Night can reduce visibility
  • Bonuses / Penalties in different weather
• 1.3.4. Agents can change terrain type

1.4. Support for Many Environment Types

Developing a flexible and extensible framework for integrating Metta AI with various gridworld environments and supporting the easy addition of new environment types.

• 1.4.1. New objects, rules, interactions can be easily added
• 1.4.2. Customizable Environment Configuration
• 1.4.3. Easy Extension for New Environment Types
• 1.4.4. Library of environments, contributors can add their own
• 1.4.5. Tools for testing new environments

1.5. Population Dynamics

Investigating the dynamics of population growth, clustering behaviors, and behavioral diversity in complex multi-agent environments, and developing tools for studying these phenomena.

• 1.5.1. Tools for Studying Population Dynamics
• 1.5.2. Clustering Behaviors and Group Formation
• 1.5.3. Balancing Game Rules for Encouraging Behavioral Diversity

1.6. Kinship Schemes

Implementing and comparing various kinship schemes, such as family structures and random kinship markers, and exploring their impact on the emergence of cooperative behaviors and social structures.

• 1.6.1. Family Structures and Inheritance
• 1.6.2. Kinship Markers
• 1.6.3. Noisy Observations of Kinship
• 1.6.4. Agents can modify Kinship

1.7. Mate Selection Schemes

Designing mate selection mechanisms based on behavioral observation and reward sharing, and studying the emergent mating strategies and population dynamics in the presence of these mechanisms.

• 1.7.1. Behavioral Observation and Evaluation
• 1.7.2. Reward Sharing Based on Mate Selection
• 1.7.3. Emergent Mating Strategies and Dynamics

2. Agent Architecture Research

Developing a robust, universal gridworld agent architecture capable of adapting to new game rules and environments.

Evaluating the effectiveness of blending reinforcement learning and imitation learning for accelerating agent training and performance.

Studying the scalability and efficiency of distributed reinforcement learning algorithms for training large-scale multi-agent systems.

2.1. Robust Universal GridWorld Agent

• 2.1.1. Adaptable Neural Network Architecture
• 2.1.2. Plug-and play memory system
• 2.1.3. Neural components can be mixed and matched without retraining

2.2. Adaptability to New Game Rules

• 2.2.1. Dynamic Action Space
• 2.2.2. Flexible Observation Processing

2.3. Dense Learning Signals

• 2.3.1. World Model Learning
• 2.3.2. Observation Prediction and Reconstruction
• 2.3.3. Auxiliary Tasks for Representation Learning

2.4. Exploration and Curiosity

Exploring the role of intrinsic motivation and curiosity in driving efficient exploration and learning in open-ended environments.

• 2.4.1. Intrinsic Motivation Signals
• 2.4.2. Novelty-based Exploration
• 2.4.3. Curiosity-driven Goal Generation

3. Scalable Training Infrastructure

3.1. Distributed Reinforcement Learning

• 3.1.1. Multi-GPU Training
• 3.1.2. Asynchronous Actor-Critic Methods
• 3.1.3. Scalable Communication Protocols

3.2. Student-Teacher Architecture

• 3.2.1. Knowledge Distillation
• 3.2.2. Curriculum Learning
• 3.2.3. Adaptive Reward Shaping

3.3. Blending Reinforcement Learning and Imitation Learning

• 3.3.1. Demonstration-guided Exploration
• 3.3.2. Behavioral Cloning for Initialization
• 3.3.3. Parallel model training with behavioral consistency pressure

4. Intelligence Evaluations for Gridworld Agents

Designing and implementing a comprehensive suite of intelligence evaluations for gridworld agents, covering navigation, maze solving, in-context learning, cooperation, and competition.

4.1. Navigation Tasks

• 4.1.1. Path Planning and Shortest Path Finding
• 4.1.2. Obstacle Avoidance and Adaptive Navigation
• 4.1.3. Multi-goal Navigation and Prioritization

4.2. Maze Solving

• 4.2.1. Procedural Maze Generation
• 4.2.2. Memory-based Maze Solving Strategies
• 4.2.3. Transfer Learning Across Maze Configurations

4.3. In-context Learning

• 4.3.1. Few-shot Task Adaptation
• 4.3.2. Meta-learning for Rapid Skill Acquisition
• 4.3.3. Compositional Task Solving

4.4. Cooperation and Competition

Investigating the impact of reward-sharing mechanisms on the emergence of cooperative behaviors in multi-agent environments.

• 4.4.1. Emergent Communication and Signaling
• 4.4.2. Cooperative Goal Achievement
• 4.4.3. Strategic Decision Making in Competitive Scenarios

5. DevOps and Tooling

5.1. Cloud Cluster Management

• 5.1.1. Automated Provisioning and Scaling
• 5.1.2. Resource Monitoring and Optimization
• 5.1.3. Fault Tolerance and Recovery

5.2. Experiment Tracking and Visualization

• 5.2.1. Hyperparameter Management
• 5.2.2. Training Progress Monitoring
• 5.2.3. Result Analysis and Comparison Tools

5.3. Continuous Integration and Deployment

• 5.3.1. Automated Testing and Validation
• 5.3.2. Containerization and Reproducibility
• 5.3.3. Pipeline Orchestration and Scheduling